End-to-end ML example from Digital Marketing Mastery Module -> builds a decision-tree classifier on the classic Iris dataset using scikit-learn.
git clone https://github.com/ChrisSim01/iris-classifier.git
cd iris-classifier
python -m venv venv
venv/scripts/activate
pip install -r requirements.txt
python src/train.py
(See project_structure.png)
Your README file should be as good as your project itself.
Make your project stand out and look professional by at least including the following elements in your README:
the name of your project
This is an extremely important component of the README. You should describe the main purpose of your project. Answer questions like “why did you build this project?” and “what problem(s) does it solve?”. It also helps to include your motivations for the project and what you learned from it.
If your project has multiple features, list them here. Don’t be afraid to brag if your project has unique features that make it stand out. You can even add screenshots and gifs to show off the features. How to use: Here, you should write step-by-step instructions on how to install and use your project. Any software or package requirements should also be listed here.
List all the technologies and/or frameworks you used and what purpose they serve in your project.
If others have contributed to your project in any way, it is important to give them credit for their work. Write your team members’ or collaborators’ names here along with a link to their GitHub profile.
It’s also important to list a license on your README so other developers can understand what they can and cannot do with your project. You can use this guide to help you choose a license.